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  "fetched_at": "2026-10-20T10:53:50Z",
  "source_url": "https://www.kaggle.com/competitions/csed226-01hw7-classification/data",
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      "content": "## **[Data Set Information]**\nBreast Cancer Wisconsin (Diagnostic) Data Set<br>\nFeatures are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. <br>\nThey describe characteristics of the cell nuclei present in the image.<br>\n\n## **[Attributes]**\n\n- Diagnosis (M = malignant, B = benign)<br>\n\nTen real-valued features are computed for each cell nucleus:<br>\n- radius (mean of distances from center to points on the perimeter)<br>\n- texture (standard deviation of gray-scale values)<br>\n- perimeter<br>\n- area<br>\n- smoothness (local variation in radius lengths)<br>\n- compactness (perimeter^2 / area - 1.0)<br>\n- concavity (severity of concave portions of the contour)<br>\n- concave points (number of concave portions of the contour)<br>\n- symmetry<br>\n- fractal dimension (\"coastline approximation\" - 1)<br>\n- Class:(2 for benign, 4 for malignant)(our target)\n",
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      "content": "# Classification Task\n\n### Input Variables:\nThere are 9 features to predict class(y-our target).\nClick Data for detail for features.\n\n### Output Variables\nThe class is our target.<br>\nClass:(2 for benign, 4 for malignant)",
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    {
      "name": "Evaluation",
      "content": "Evaluation metirc is **Accuracy**.\n\ncf)\n\nfrom sklearn.metrics import accuracy_score",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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